Once the video subtitles are introduced to the viewport prediction model, its direct and indirect contributions are recognized. The direct contribution can be seen when the features of video subtitle, namely, subtitle indicator and navigation features are considered as input features of the proposed model. The indirect contribution can be seen when the video subtitles influence the user’s head trajectory. Once the coordinates of the user's viewing trajectory are collected in the experiments, the video subtitles will navigate the users to look for some specific content in a particular region in the video frame. Then these trajectory coordinates are used to generate ground-truth maps, which will be extracted as saliency features. In the previous studies [1]}, [2]}, [3]}, their saliency maps are only generated from the video content, which does not necessarily coincide with the users' real viewport when the video subtitles exist. Therefore, this supports the idea which states that the saliency features are also influenced by the video subtitles. In the proposed method, it can be seen that the video subtitles indirectly influence the user’s head trajectory, which additionally contributes to the improvement of viewport prediction’s accuracy.
